Thursday, March21 Session 1: Brain Architecture

Markus Diesmann
Jülich Research Centre and JARA Institute I Jülich(NEST)

Title
Construction and simulation of an open cortical multi-area model atcellular resolution

Abstract
The mammalian brain exhibits universality. In evolution the cortex has increasedin volume from mouse to man by three orders of magnitude, while thearchitecture at the local scale of a cubic millimeter has been largely conserved interms of the multi-layered structure and the density of synapses. Furthermore,
local cortical networks are similar independently of whether an area processes
visual, auditory, or tactile information. At the brain scale, the areas again form ahierarchical recurrent network, and the area-specific cellular and laminar
composition of the network is related to the connectivity between areas. Thisgives us hope that fundamental mechanisms of cortical computation can bediscovered. So far an integrated view of the construction principles is, however,missing. Our recent modeling study [1] integrates architectural and axonal
tracing data into a multi-scale framework describing one hemisphere of macaquevision-related cortex at cellular resolution. This brain structure contains about 1
billion neurons, the limit of what supercomputers can simulate today but too
costly for routine work. Therefore, our model represents each of the 32 areas byjust one microcircuit of about 100,000 neurons. Simulations [2] confirm thatbrain activity is compatible with in-vivo resting-state data on multiple levels fromspiking activity to fMRI. Further increasing model size requires progress insimulation technology. Code [3] developed in collaboration with the post-K
computer project makes the memory consumption of the individual computenodes of a supercomputer fully independent of total network size, as needed forexascalesystems.

Still,

themicroscopicallyparallelbrain

challenges

supercomputers. Achieving real-time or accelerated speeds as desirable forstudies of plasticity and learning is difficult. Neuromorphic computing promises
an alternative as we recently indicated by porting a full-scale microcircuit modelto SpiNNaker [4]. Brain-scale models require the collaboration of researchers
because of the amount and heterogeneity of the data that need to be aggregated.
Furthermore, the executable model description is not sufficient for the publicationof findings. Researchers can only add data to the model or modify assumptions
if they have access to the process of model construction. Borrowing techniquesfrom computer science and systems biology [5] we present a digitized workflow
of model construction reproducing all figures of our respective publications. We
are confident that the open digital availability of brain-scale models serves thedevelopment of neuromorphic hardware by providing science cases and platforms
for the integration of ideas on particular brain functions. The repository of themodel is https://inm-6.github.io/multi-area-model and a tutorial video is locatedat https://youtu.be/YsH3BcyZBcU.
This research used resources of the K computer at the RIKEN AICS. Supportedby the project Exploratory Challenge on Post-K Computer (Understanding theneural mechanisms of thoughts and its applications to AI) of MEXT. Use of theJUQUEEN supercomputer in Jülich was made possible by VSR computation timegrant JINB33. The development of NEST is guided by the NEST Initiative. Partial
funding comes from the Human Brain Project through European Union 7thFramework Program grant 604102 (Ramp Up Phase) and EU Horizon 2020
research and innovation program grants 720270 (SGA1) and 785907 (SGA2).
[1] Schmidt M., Bakker R., Hilgetag CC., Diesmann M., van Albada SJ (2018)
Brain Struct Func 223(3):1409-1435[2] Schmidt M, Bakker R, Shen K, Bezgin G, Hilgetag CC, Diesmann M, vanAlbada SJ (2018) PLOS Comput Biol 14(10):e1006359[3] Jordan J, Ippen T, Helias M, Kitayama I, Mitsuhisa S, Igarashi J, DiesmannM, Kunkel S (2018) Front Neuroinform 12:2
[4] van Albada SJ, Rowley AG, Senk J, Hopkins M, Schmidt M, Stokes AB,Lester DR, Diesmann M, Furber SB (2018) Front Neurosci 12:291[5] Köster J, Rahmann S (2012) Bioinform 28:2520-2522

BiographyProf. Dr. Markus Diesmann is director of the Institute of Neuroscience andMedicine (INM-6, Computational and Systems Neuroscience), director of theInstitute for Advanced Simulation (IAS-6, Theoretical Neuroscience) and directorof the JARAInstitute Brain structure-function relationships (INM-10) at Jülich
Research Centre, Germany. He is also full professor in Computational
Neuroscience at the School of Medicine, RWTH University Aachen, Germany andaffiliated with the Department of Physics at the same university. Prof. Diesmannstudied physics at Ruhr University Bochum with a year of Cognitive Science atUniversity of Sussex, UK. He carried out his PhD studies at Weizmann Instituteof Science, Rehovot, Israel, and Albert-Ludwigs-University Freiburg. In 2002 hereceived his PhD degree from the Faculty of Physics, Ruhr-University Bochum,Germany. From 1999 Prof. Markus Diesmann worked as senior staff at
Department of Nonlinear Dynamics, Max-Planck-Institute for Dynamics and SelfOrganization, Göttingen, Germany. In 2003 he became assistant professor ofComputational Neurophysics at Albert-Ludwigs-University, Freiburg, Germany
before in 2006 joining the RIKEN Brain Science Institute, Wako City, Japan as aunit leader and later team leader. In 2011 Markus Diesmann moved to Jülich. His
main scientific interests include the correlation structure of neuronal networks,
models of cortical networks, simulation technology and supercomputing. He is
one of the original authors of the NEST simulation code and a member of thesteering committee of the NEST Initiative.